Autonomous or semi-autonomous robotic devices are increasingly used within consumer homes and commercial establishments. Such devices may include a robotic vacuum cleaner, lawn mower, mop, or other similar devices. To operate autonomously or to operate with minimal (or less than fully manual) input and/or external control within a working environment, mapping methods are implemented within robotic devices such that the robotic device may autonomously create a map of the working environment and subsequently (or concurrently) use it for navigation.
Mapping methods using different measurement systems for robotic devices have been previously proposed. For example, Simultaneous Localization And Mapping (SLAM) is a mapping method which captures large amounts of feature points to create and continuously update a map of the working environment.
Many SLAM solutions rely on laser distance sensors (LDS) with very high rates of data collection. Such LDS sensors often provide thousands of readings per second in a 360-degrees field of view. Other SLAM solutions such as VSLAM solutions rely heavily on image processing techniques for extracting features such as circles, arcs, lines, and corners from captured images. Images are often processed through multiple stages, usually beginning with simple image filters and then later are processed using algorithms such as corner detection. These SLAM methods frequently have additional stages for probabilistic processing and particle filtering as well, thus, requiring substantial memory for keeping multiple sets of redundant data. These methods are also limited by their poor functionality when operating in areas where the ambient level of light is lower than a certain threshold or in areas with glass walls, as detecting features on transparent or highly reflective surfaces is challenging for the sensors used.
Furthermore, many image processing techniques are computationally expensive, requiring powerful CPUs or dedicated MCUs. This results in robotic devices with SLAM capability to be up to three or four times the cost of ones without such capability. Also, some implemented SLAM solutions require an additional piece of equipment to project an infrared (IR) pattern onto a surface. For example, some products use an additional stationary and external device system that projects an IR pattern onto the ceiling or the environment to determine the position of the robotic device. It is evident that these well-established mapping methods require large amounts of memory, substantial processing power, and high costs for implementation.
Distance sensors may also be used in creating a depth map of the environment. However, in certain cases, the depth maps constructed may be incomplete, containing gaps in areas where information is lacking. The gaps may be due to an opening in the wall, blind spots unseen by the measuring device, or a lack of data resulting from a camera with inadequate detection range. A complete closed loop map of the environment may be necessary to ensure coverage of the entire environment. The majority of distance sensor-based mapping methods do not provide details of a method for identifying and closing gaps in the map after an initial mapping attempt or require the use of additional equipment to close gaps in the map.
None of the preceding discussion should be taken as a disclaimer of any of the described techniques, as the present approach may be used in combination with these other techniques in some embodiments.